Natural Language Processing (NLP)-Based Detection of Depressive Comments and Tweets: A Text Classification Approach

Jose C. Agoylo, Kim N. Subang and Jorton A. Tagud
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Jose C. Agoylo: BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines
Kim N. Subang: BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines
Jorton A. Tagud: BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines

International Journal of Latest Technology in Engineering, Management & Applied Science, 2024, vol. 13, issue 6, 37-43

Abstract: Depression is a major mental health problem that affects millions globally, causing significant emotional distress and impacting quality of life. With the pervasive use of social media platforms, individuals often express their thoughts and emotions through online posts, comments, and tweets, presenting an opportunity to study and detect depressive language patterns. This research utilized the dataset from Kaggle between December 2019 and December 2020, which originated largely from India. This paper presents a novel approach for detecting depressive sentiment in online discourse using Natural Language Processing (NLP) and machine learning techniques. The study aims to develop an automated system capable of accurately identifying depressive comments and tweets, facilitating early intervention and support for individuals potentially struggling with mental health challenges. The proposed methodology will be rigorously evaluated using standard performance metrics, including precision, recall, F1- score, and ROC curve. The study will also conduct qualitative analyses to gain insights into the types of textual patterns and linguistic cues most indicative of depressive sentiment. The results of our study are promising, with a maximum validation accuracy of 0.88 demonstrating the model's ability to classify depressive and non-depressive comments and tweets accurately. The outcomes of this research have significant implications for mental health monitoring and intervention strategies. By accurately detecting depressive sentiment in online discourse, healthcare professionals and support services can proactively reach out to individuals exhibiting potential signs of depression, fostering early intervention and improving overall mental health outcomes.

Date: 2024
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